scholarly journals Passive Earth Observations of Volcanic Clouds in the Atmosphere

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 199 ◽  
Author(s):  
Fred Prata ◽  
Mervyn Lynch
Keyword(s):  
Spatial Information ◽  
Physical Models ◽  
Optical Parameters ◽  
Cloud Detection ◽  
Volcanic Emissions ◽  
Temporal Behaviour ◽  
Statistical Measures ◽  
Volcanic Clouds ◽  
Infrared Measurements ◽  
Volcanic Cloud

Current Earth Observation (EO) satellites provide excellent spatial, temporal and spectral coverage for passive measurements of atmospheric volcanic emissions. Of particular value for ash detection and quantification are the geostationary satellites that now carry multispectral imagers. These instruments have multiple spectral channels spanning the visible to infrared (IR) wavelengths and provide 1 × 1 km2 to 4 × 4 km2 resolution data every 5–15 min, continuously. For ash detection, two channels situated near 11 and 12 μ m are needed; for ash quantification a third or fourth channel also in the infrared is useful for constraining the height of the ash cloud. This work describes passive EO infrared measurements and techniques to determine volcanic cloud properties and includes examples using current methods with an emphasis on the main difficulties and ways to overcome them. A challenging aspect of using satellite data is to design algorithms that make use of the spectral, temporal (especially for geostationary sensors) and spatial information. The hyperspectral sensor AIRS is used to identify specific molecules from their spectral signatures (e.g., for SO2) and retrievals are demonstrated as global, regional and hemispheric maps of AIRS column SO2. This kind of information is not available on all sensors, but by combining temporal, spatial and broadband multi-spectral information from polar and geo sensors (e.g., MODIS and SEVIRI) useful insights can be made. For example, repeat coverage of a particular area using geostationary data can reveal temporal behaviour of broadband channels indicative of eruptive activity. In many instances, identifying the nature of a pixel (clear, cloud, ash etc.) is the major challenge. Sophisticated cloud detection schemes have been developed that utilise statistical measures, physical models and temporal variation to classify pixels. The state of the art on cloud detection is good, but improvements are always needed. An IR-based multispectral cloud identification scheme is described and some examples shown. The scheme is physically based but has deficiencies that can be improved during the daytime by including information from the visible channels. Physical retrieval schemes applied to ash detected pixels suffer from a lack of knowledge of some basic microphysical and optical parameters needed to run the retrieval models. In particular, there is a lack of accurate spectral refractive index information for ash particles. The size distribution of fine ash (1–63 μ m, diameter) is poorly constrained and more measurements are needed, particularly for ash that is airborne. Height measurements are also lacking and a satellite-based stereoscopic height retrieval is used to illustrate the value of this information for aviation. The importance of water in volcanic clouds is discussed here and the separation of ice-rich and ash-rich portions of volcanic clouds is analysed for the first time. More work is required in trying to identify ice-coated ash particles, and it is suggested that a class of ice-rich volcanic cloud be recognized and termed a ‘volcanic ice’ cloud. Such clouds are frequently observed in tropical eruptions of great vertical extent (e.g., 8 km or higher) and are often not identified correctly by traditional IR methods (e.g., reverse absorption). Finally, the global, hemispheric and regional sampling of EO satellites is demonstrated for a few eruptions where the ash and SO 2 dispersed over large distances (1000s km).

scholarly journals A Daytime Complement to the Reverse Absorption Technique for Improved Automated Detection of Volcanic Ash

2006 ◽  
Vol 23 (11) ◽  
pp. 1422-1444 ◽  
Author(s):  
Michael J. Pavolonis ◽  
Wayne F. Feltz ◽  
Andrew K. Heidinger ◽  
Gregory M. Gallina
Keyword(s):  
Brightness Temperature ◽  
Temperature Difference ◽  
Detection Algorithm ◽  
Detection Sensitivity ◽  
False Alarms ◽  
Cloud Detection ◽  
Cloud Optical Depth ◽  
Absorption Signal ◽  
Volcanic Clouds ◽  
Volcanic Cloud

Abstract An automated volcanic cloud detection algorithm that utilizes four spectral channels (0.65, 3.75, 11, and 12 μm) that are common among several satellite-based instruments is presented. The new algorithm is physically based and globally applicable and can provide quick information on the horizontal location of volcanic clouds that can be used to improve real-time ash hazard assessments. It can also provide needed input into volcanic cloud optical depth and particle size retrieval algorithms, the products of which can help improve ash dispersion forecasts. The results of this new four-channel algorithm for several scenes were compared to a threshold-based reverse absorption algorithm, where the reverse absorption algorithm is used to identify measurements with a negative 11–12-μm brightness temperature difference. The results indicate that the new four-channel algorithm is not only more sensitive to the presence of volcanic clouds but also generally less prone to false alarms than the standard reverse absorption algorithm. The greatest impact on detection sensitivity is seen in the Tropics, where water vapor can often mask the reverse absorption signal. The four-channel algorithm was able to detect volcanic clouds even when the 11–12-μm brightness temperature difference was greater than +2 K. In the higher latitudes, the greatest impact seen was the significant reduction in false alarms compared to the reverse absorption algorithm and the improved ability to detect optically thick volcanic clouds. Cloud water can also mask the reverse absorption signal. The four-channel algorithm was shown to be more sensitive to volcanic clouds that have a water (ice or liquid water) component than the reverse absorption algorithm.

The 2015 Calbuco Volcanic Cloud Detection Using GNSS Radio Occultation and Satellite Lidar

2020 ◽  
Author(s):  
Pierre-Yves Tournigand ◽  
Valeria Cigala ◽  
Alfredo J. Prata ◽  
Andrea K. Steiner ◽  
Gottfried Kirchengast ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Cloud Detection ◽  
Volcanic Cloud

scholarly journals Influence of Detector Size and Positioning on Near-Infrared Measurements and Iso-pathlength Point of Turbid Materials

2021 ◽  
Vol 9 ◽  
Author(s):  
Hamootal Duadi ◽  
Idit Feder ◽  
Dror Fixler
Keyword(s):  
Critical Parameter ◽  
Near Infrared ◽  
Tissue Oxygenation ◽  
Experimental System ◽  
Optical Path ◽  
Optical Parameters ◽  
Infrared Measurements ◽  
Profile Changes ◽  
Physical Phenomena ◽  
Crossing Point

Measuring physical phenomena in an experimental system is commonly limited by the detector. When dealing with spatially defined behaviors, the critical parameter is the detector size. In this work, we examine near-infrared (NIR) measurements of turbid media using different size detectors at different positions. We examine cylindrical and semi-infinite scattering samples and measure their intensity distribution. An apparent crossing point between samples with different scatterings was previously discovered and named the iso-pathlength point (IPL). Monte Carlo simulations show the expected changes due to an increase in detector size or similarly as the detector’s location is distanced from the turbid element. First, the simulations show that the intensity profile changes, as well as the apparent IPL. Next, we show the average optical pathlength, and as a result, the differential pathlength factor, are mostly influenced by the detector size in the range close to the source. Experimental measurements using different size detectors at different locations validate the influence of these parameters on the intensity profiles and apparent IPL point. These findings must be considered when assessing optical parameters based on multiple scattering models. In cases such as NIR assessment of tissue oxygenation, size and location may cause false results for absorption or optical path.

scholarly journals Early in-flight detection of SO<sub>2</sub> via Differential Optical Absorption Spectroscopy: a feasible aviation safety measure to prevent potential encounters with volcanic plumes

2011 ◽  
Vol 4 (3) ◽  
pp. 2827-2881 ◽  
Author(s):  
L. Vogel ◽  
B. Galle ◽  
C. Kern ◽  
H. Delgado Granados ◽  
V. Conde ◽  
...  
Keyword(s):  
Optical Absorption ◽  
Absorption Spectroscopy ◽  
Volcanic Ash ◽  
Lower Troposphere ◽  
Differential Optical Absorption Spectroscopy ◽  
Cumulative Exposure ◽  
Optical Absorption Spectroscopy ◽  
Volcanic Plumes ◽  
Volcanic Clouds ◽  
Volcanic Cloud

Abstract. Volcanic ash constitutes a risk to aviation, mainly due to its ability to cause jet engines to fail. Other risks include the possibility of abrasion of windshields and potentially serious damage to avionic systems. These hazards have been widely recognized since the early 1980s, when volcanic ash provoked several incidents of engine failure in commercial aircraft. In addition to volcanic ash, volcanic gases also pose a threat. Prolonged and/or cumulative exposure to sulphur dioxide (SO2) or sulphuric acid (H2SO4) aerosols potentially affects e.g. windows, air frame and may cause permanent damage to engines. SO2 receives most attention among the gas species commonly found in volcanic plumes because its presence above the lower troposphere is a clear proxy for a volcanic cloud and indicates that fine ash could also be present. Up to now, remote sensing of SO2 via Differential Optical Absorption Spectroscopy (DOAS) in the ultraviolet spectral region has been used to measure volcanic clouds from ground based, airborne and satellite platforms. Attention has been given to volcanic emission strength, chemistry inside volcanic clouds and measurement procedures were adapted accordingly. Here we present a set of experimental and model results, highlighting the feasibility of DOAS to be used as an airborne early detection system of SO2 in two spatial dimensions. In order to prove our new concept, simultaneous airborne and ground-based measurements of the plume of Popocatépetl volcano, Mexico, were conducted in April 2010. The plume extended at an altitude around 5250 m above sea level and was approached and traversed at the same altitude with several forward looking DOAS systems aboard an airplane. These DOAS systems measured SO2 in the flight direction and at ± 40 mrad (2.3°) angles relative to it in both, horizontal and vertical directions. The approaches started at up to 25 km distance to the plume and SO2 was measured at all times well above the detection limit. In combination with radiative transfer studies, this study indicates that an extended volcanic cloud with a concentration of 1012 molecules cm−3 at typical flight levels of 10 km can be detected unambiguously at distances of up to 80 km away. This range provides enough time (approx. 5 min) for pilots to take action to avoid entering a volcanic cloud in the flight path, suggesting that this technique can be used as an effective aid to prevent dangerous aircraft encounters with potentially ash rich volcanic clouds.

A CNN based Hybrid approach towards automatic image registration

2013 ◽  
Vol 62 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Pattathal V. Arun ◽  
Sunil K. Katiyar
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Feature Matching ◽  
Spatial Information ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Cellular Neural Network ◽  
Contextual Knowledge ◽  
Neural Network Approach ◽  
Statistical Measures

Abstract Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using corset optimization The salient features of this work are cellular neural network approach based SIFT feature point optimisation, adaptive resampling and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. System has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

scholarly journals Spectral and Spatial Cloud Detection Onboard for Hyperspectral Remote Sensing Image

2017 ◽  
Author(s):  
Haoyang Li ◽  
Hong Zheng ◽  
Chuanzhao Han ◽  
Haibo Wang ◽  
Min Miao
Keyword(s):  
Spatial Information ◽  
Classification Performance ◽  
Hyperspectral Images ◽  
Detection Methods ◽  
Cloud Detection ◽  
Dynamic Stochastic Resonance ◽  
Manual Intervention ◽  
Dynamic Stochastic ◽  
Hyperspectral Classification ◽  
Cloud Mask

It is strongly desirable to accurately detect the clouds in hyperspectral images onboard before compression. However, conventional onboard cloud detection methods are not appropriate to all situation such as shadowed cloud or darken snow covered surfaces which are not identified properly in the NDSI test. In this paper, we propose a new spectral&ndash;spatial classification strategy to enhance the orbiting cloud screen performances obtained on hyperspectral images by integrating threshold exponential spectral angle map (TESAM), adaptive Markov random field (aMRF) and dynamic stochastic resonance (DSR). TESAM is performed to classify the cloud pixels coarsely based on spectral information. Then aMRF is performed to do optimal process by using spatial information, which improved the classification performance significantly. Some misclassification points still exist after aMRF processing because of the noisy data in the onboard environment. DSR is used to eliminate misclassification points in binary labeling image after aMRF. Taking level 0.5 data from hyperion as dataset, the average overall accuracy of the proposed algorithm is 96.28% after test. The method can provide cloud mask for the on-going EO-1 images and related satellites with the same spectral settings without manual intervention. The experiment indicate that the proposed method reveals better performance than the classical onboard cloud detection or current state-of-the-art hyperspectral classification methods.

scholarly journals Composition and evolution of volcanic aerosol from eruptions of Kasatochi, Sarychev and Eyjafjallajökull in 2008–2010 based on CARIBIC observations

2013 ◽  
Vol 13 (4) ◽  
pp. 1781-1796 ◽  
Author(s):  
S. M. Andersson ◽  
B. G. Martinsson ◽  
J. Friberg ◽  
C. A. M. Brenninkmeijer ◽  
A. Rauthe-Schöch ◽  
...  
Keyword(s):  
Volcanic Eruptions ◽  
Volcanic Aerosol ◽  
Volcanic Clouds ◽  
Tropopause Region ◽  
Volcanic Cloud ◽  
High Concentrations ◽  
The Relationship ◽  
Volcanic Influence ◽  
Large Volcanic Eruptions ◽  
Observation Period

Abstract. Large volcanic eruptions impact significantly on climate and lead to ozone depletion due to injection of particles and gases into the stratosphere where their residence times are long. In this the composition of volcanic aerosol is an important but inadequately studied factor. Samples of volcanically influenced aerosol were collected following the Kasatochi (Alaska), Sarychev (Russia) and also during the Eyjafjallajökull (Iceland) eruptions in the period 2008–2010. Sampling was conducted by the CARIBIC platform during regular flights at an altitude of 10–12 km as well as during dedicated flights through the volcanic clouds from the eruption of Eyjafjallajökull in spring 2010. Elemental concentrations of the collected aerosol were obtained by accelerator-based analysis. Aerosol from the Eyjafjallajökull volcanic clouds was identified by high concentrations of sulphur and elements pointing to crustal origin, and confirmed by trajectory analysis. Signatures of volcanic influence were also used to detect volcanic aerosol in stratospheric samples collected following the Sarychev and Kasatochi eruptions. In total it was possible to identify 17 relevant samples collected between 1 and more than 100 days following the eruptions studied. The volcanically influenced aerosol mainly consisted of ash, sulphate and included a carbonaceous component. Samples collected in the volcanic cloud from Eyjafjallajökull were dominated by the ash and sulphate component (∼45% each) while samples collected in the tropopause region and LMS mainly consisted of sulphate (50–77%) and carbon (21–43%). These fractions were increasing/decreasing with the age of the aerosol. Because of the long observation period, it was possible to analyze the evolution of the relationship between the ash and sulphate components of the volcanic aerosol. From this analysis the residence time (1/e) of sulphur dioxide in the studied volcanic cloud was estimated to be 45 ± 22 days.

scholarly journals CDUNet: Cloud Detection UNet for Remote Sensing Imagery

2021 ◽  
Vol 13 (22) ◽  
pp. 4533
Author(s):  
Kai Hu ◽  
Dongsheng Zhang ◽  
Min Xia
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Spatial Location ◽  
Spatial Position ◽  
Detection Methods ◽  
Cloud Detection ◽  
Position Information ◽  
Deep Learning Network

Cloud detection is a key step in the preprocessing of optical satellite remote sensing images. In the existing literature, cloud detection methods are roughly divided into threshold methods and deep-learning methods. Most of the traditional threshold methods are based on the spectral characteristics of clouds, so it is easy to lose the spatial location information in the high-reflection area, resulting in misclassification. Besides, due to the lack of generalization, the traditional deep-learning network also easily loses the details and spatial information if it is directly applied to cloud detection. In order to solve these problems, we propose a deep-learning model, Cloud Detection UNet (CDUNet), for cloud detection. The characteristics of the network are that it can refine the division boundary of the cloud layer and capture its spatial position information. In the proposed model, we introduced a High-frequency Feature Extractor (HFE) and a Multiscale Convolution (MSC) to refine the cloud boundary and predict fragmented clouds. Moreover, in order to improve the accuracy of thin cloud detection, the Spatial Prior Self-Attention (SPSA) mechanism was introduced to establish the cloud spatial position information. Additionally, a dual-attention mechanism is proposed to reduce the proportion of redundant information in the model and improve the overall performance of the model. The experimental results showed that our model can cope with complex cloud cover scenes and has excellent performance on cloud datasets and SPARCS datasets. Its segmentation accuracy is better than the existing methods, which is of great significance for cloud-detection-related work.

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